Advanced metering infrastructure (AMI) is an integrated network of sensors, smart meters, and software that empowers end users to monitor and control utilities such as water, gas, and electricity. AMI systems enable the measurement and visualization of time-specific data in real-time, which, combined with remote control capabilities, can help companies and households reduce overhead costs and more precisely track resource consumption. The application of AMI must be complemented by the utilization of advanced security systems to ensure that data and control capabilities cannot be tampered with. This is important because both direct billing and operational decisions are often determined by the data provides by an AMI system.

Advanced planning and scheduling (APS, also known as advanced manufacturing) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities.

Autonomous robots are intelligent machines capable of performing tasks in the world independently of either direct human control or fixed programming. Examples range from autonomous drones, to industrial production robots, to your robotic vacuum cleaner. They combine expertise from the fields of artificial intelligence, robotics, and information science.The autonomous robot must have the ability to perceive its environment, analyze situational data in order to make decisions based on what it perceives, and then modify its actions based on these decisions. For example, the scope of autonomy could include starting, stopping, maneuvering around obstacles, communicating to obstacles, and using appendages to manipulate obstacles. There are few autonomous robots in operation today. Even most sophisticated, dynamic robots such as those used in an automotive factory perform according to static programming. And most autonomous robots are only semi-autonomous and will likely remain so even as more fundamental autonomy becomes technically feasible. For example, the Roomba vacuum cleaner does not move according to a pre-programmed route and can modify its route dynamically as its environment changes. However, it has a very limited degree of freedom that is determined by its programming.

Building energy management systems (BEMS) provide real-time remote monitoring and integrated control of a wide range of connected systems, allowing modes of operation, energy use, and environmental conditions to be monitored and modified based on hours of operation, occupancy, or other variables to optimise efficiency and comfort. Building energy management systems can also trigger alarms, in some cases predicting problems and informing maintenance programmes. They maintain records of historical performance to enable benchmarking of performance against other buildings or across time and may help automate report writing. BEMS are often integrated with building automation and control (BAC) systems, which have a broaded scope of operations.

Fleet management is an administrative approach that allows companies to organize and coordinate work vehicles to improve efficiency, reduce costs, and provide compliance with government regulations. While most commonly used for vehicle tracking, fleet management includes other use cases such as mechanical diagnostics and driver behavior. Automated fleet management solutionsto connect vehicles and monitor driver activities, allowing managers to gain insight into fleet performance and driver behavior. This enables managers to know where vehicles and drivers are at all times, identify potential problems and mitigate risks before they become larger issues that can jeopardize client satisfaction, impact driver safety, or increase costs.

A flexible manufacturing system is a production method that is designed to easily adapt to changes in the type and quantity of the product being manufactured. Machines and computerized systems can be configured to manufacture a variety of parts and handle changing levels of production.

Fog computing refers to a decentralized computing structure, where resources, including the data and applications, get placed in logical locations between the data source and the cloud; it also is known by the terms fogging and fog networking. The goal of this is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data needs to be transported on the network, improving overall network efficiency and performance. Fog computing can also be deployed for security reasons, as it has the ability to segment bandwidth traffic and introduce additional firewalls to a network for higher security.

Machine condition monitoring is the process of monitoring parameters such as vibration and temperature in order to identify changes that indicate a reduction in performance or impending fault. It is a necessary component of predictive maintenance solutions and allows maintenance to be scheduled prior to failure, or other actions to be taken to prevent damages to the machine and loss of production. Condition monitoring also provides value beyond improving maintenance schedules. For example, improved visibility into machine operations can indicate the root causes of product defects and can support optimization of energy consumption.

Manufacturing system automation integrates software and machinery so that manufacturing processes are run autonomously through computer programming. The goal of manufacturing system automation is to minimize the amount of human assistance needed in the manufacturing process. These systems provide constant feedback loops and adjust controlling parameteres in response to feedback from PLCs and smart sensors installed on machinery. Sensors are commonly embedded in new equipment or can be installed on legacy equipment. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices and computers, usually in combination. The benefit of automation includes a reduction of costs related to labor, electricity, water, gas, and scrap, as well as improvements to quality, accuracy, and precision. Manufacturing system automation can also reduce changeovertimes, thereby enabling small batch size production and mass customization.

Mass customization is a manufacturing technique which combines the flexibility and personalization of custom-made products with the low unit costs associated with mass production. Mass customization can be viewed as collaborative efforts between customers and manufacturers, who have different sets of priorities and need to jointly search for solutions that best match customers' individual specific needs with manufacturers' customization capabilities. The objective is to enable customization based on customer requirements without a corresponding increase in production costs. At its limit, it is the mass production of individually customized goods. However, it is more commonly applied to small batch sizes that meet the requirements of specific market niches. A key premise of mass customization is the imperative to postpone the task of differentiating a product for a specific customer until the latest possible point in the supply network. However, process improvements must also be supported by investments in technology that enable the tracking of unique products or batches and flexible production processes.

Predictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.

Process control and optimization (PCO) is the discipline of adjusting a process to maintain or optimize a specified set of parameters without violating process constraints. The PCO market is being driven by rising demand for energy-efficient production processes, safety and security concerns, and the development of IoT systems that can reliably predict process deviations. Fundamentally, there are three parameters that can be adjusted to affect optimal performance. - Equipment optimization: The first step is to verify that the existing equipment is being used to its fullest advantage by examining operating data to identify equipment bottlenecks.- Operating procedures: Operating procedures may vary widely from person-to-person or from shift-to-shift. Automation of the plant can help significantly. But automation will be of no help if the operators take control and run the plant in manual.- Control optimization: In a typical processing plant, such as a chemical plant or oil refinery, there are hundreds or even thousands of control loops. Each control loop is responsible for controlling one part of the process, such as maintaining a temperature, level, or flow. If the control loop is not properly designed and tuned, the process runs below its optimum. The process will be more expensive to operate, and equipment will wear out prematurely. For each control loop to run optimally, identification of sensor, valve, and tuning problems is important. It has been well documented that over 35% of control loops typically have problems. The process of continuously monitoring and optimizing the entire plant is sometimes called performance supervision.

Structural health monitoring solutions ensure the safety and soundness of engineering structures such as a buildings and bridges. Structural health monitoring uses an assortment of sensors to collect and analyze data pertaining to any damage or deterioration that a structure may receive over the course of its life. The data that structural health monitoring systems acquire can help its users avoid structural failures and changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. The structural health monitoring process involves the observation of a system over time using periodically sampled response measurements from an array of sensors (often inertial accelerometers), the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. For long term solutions, the output of this process is periodically updated information regarding the ability of the structure to perform its intended function in light of the inevitable aging and degradation resulting from operational environments. After extreme events, such as earthquakes or blast loading, health monitoring is used for rapid condition screening and aims to provide, in near real time, reliable information regarding the integrity of the structure.

A time-sensitive network (TSN) is a set of Ethernet standards that will allow time-synchronized low latency streaming services through 802 networks. TSN focuses on creating a convergence between information technology (IT) and industrial operational technology (OT) by extending and adapting existing Ethernet standards. It adds the concept of time to networks so that messages can be delivered within a specific time frame. TSN technology aims to standardize features on OSI-Layer 2 in order that different protocols can share the same infrastructure. TSN as a communication system can achieve its full potential. The three basic components are:1. Time synchronization: All devices that are participating in real-time communication need to have a common understanding of time2. Scheduling and traffic shaping: All devices that are participating in real-time communication adhere to the same rules in processing and forwarding communication packets3. Selection of communication paths, path reservations and fault-tolerance: All devices that are participating in real-time communication adhere to the same rules in selecting communication paths and in reserving bandwidth and time slots, possibly utilizing more than one simultaneous path to achieve fault-tolerance

Warehouse automation is the application of specialized equipment and storage and retrieval systems to automate warehousing tasks previously handled by manual labor. Warehouse automation takes many forms, including machines and robots that aid workers with processes related to inventory handling, sensors that track goods, and software that automates record keeping. Leveraging warehouse automation solutions can help warehouses increase productivity, improve the accuracy of inventory records, reduce labor costs, and improve safety.

Water utility management systems monitor and collect data on the infrastructure used to store and deliver water to improve the efficiency of water delivery to customers. Aging infrastructure means that the vast majority of the reticulation network that delivers water to customers have been invisible to the utility company. By placing IoT sensors on water utility infrastructure, the utility operator can collect data on the water flowing between the different points to detect leakages, shortages, quality levels, and consumption levels. Sensors can also assess the condition of infrastructure to enable preventative and predictive maintenance.